{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recurrent Neural Networks Models\n",
    "In this notebook, we show an example of how RNNs can be used with darts.\n",
    "If you are new to darts, we recommend you first follow the `darts-intro.ipynb` notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "\n",
    "fix_pythonpath_if_working_locally()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import shutil\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.models import RNNModel, ExponentialSmoothing, BlockRNNModel\n",
    "from darts.metrics import mape\n",
    "from darts.utils.statistics import check_seasonality, plot_acf\n",
    "from darts.datasets import AirPassengersDataset, SunspotsDataset\n",
    "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recurrent Models\n",
    "\n",
    "Darts includes two recurrent forecasting model classes: `RNNModel` and `BlockRNNModel`. \n",
    "\n",
    "`RNNModel` is fully recurrent in the sense that, at prediction time, an output is computed using these inputs:\n",
    "\n",
    "- the previous target value, which will be set to the last known target value for the first prediction,\n",
    "  and for all other predictions it will be set to the previous prediction\n",
    "- the previous hidden state\n",
    "- the current covariates (if the model was trained with covariates)\n",
    "\n",
    "A prediction with forecasting horizon `n` thus is created in `n` iterations of `RNNModel` predictions and requires `n` future covariates to be known. This model is suited for forecasting problems where the target series is highly dependent on covariates that are known in advance.\n",
    "\n",
    "`BlockRNNModel` has a recurrent encoder stage, which encodes its input, and a fully-connected neural network decoder stage, which produces a prediction of length `output_chunk_length` based on the last hidden state of the encoder stage. Consequently, this model produces 'blocks' of forecasts and is restricted to looking at covariates with the same time index as the input target series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Air Passenger Example\n",
    "This is a data set that is highly dependent on covariates. Knowing the month tells us a lot about the seasonal component, whereas the year determines the effect of the trend component. Both of these covariates are known in the future, and thus the `RNNModel` class is the preferred choice for this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data:\n",
    "series = AirPassengersDataset().load()\n",
    "\n",
    "# Create training and validation sets:\n",
    "train, val = series.split_after(pd.Timestamp(\"19590101\"))\n",
    "\n",
    "# Normalize the time series (note: we avoid fitting the transformer on the validation set)\n",
    "transformer = Scaler()\n",
    "train_transformed = transformer.fit_transform(train)\n",
    "val_transformed = transformer.transform(val)\n",
    "series_transformed = transformer.transform(series)\n",
    "\n",
    "# create month and year covariate series\n",
    "year_series = datetime_attribute_timeseries(\n",
    "    pd.date_range(start=series.start_time(), freq=series.freq_str, periods=1000),\n",
    "    attribute=\"year\",\n",
    "    one_hot=False,\n",
    ")\n",
    "year_series = Scaler().fit_transform(year_series)\n",
    "month_series = datetime_attribute_timeseries(\n",
    "    year_series, attribute=\"month\", one_hot=True\n",
    ")\n",
    "covariates = year_series.stack(month_series)\n",
    "cov_train, cov_val = covariates.split_after(pd.Timestamp(\"19590101\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train an LSTM neural net. For using vanilla RNN or GRU instead, replace `'LSTM'` by `'RNN'` or `'GRU'`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "my_model = RNNModel(\n",
    "    model=\"LSTM\",\n",
    "    hidden_dim=20,\n",
    "    dropout=0,\n",
    "    batch_size=16,\n",
    "    n_epochs=300,\n",
    "    optimizer_kwargs={\"lr\": 1e-3},\n",
    "    model_name=\"Air_RNN\",\n",
    "    log_tensorboard=True,\n",
    "    random_state=42,\n",
    "    training_length=20,\n",
    "    input_chunk_length=14,\n",
    "    force_reset=True,\n",
    "    save_checkpoints=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In what follows, we can just provide the whole `covariates` series as `future_covariates` argument to the model; the model will slice these covariates and use only what it needs in order to train on forecasting the target `train_transformed`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67f94871ce5541c59f858b450b669fa5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/300 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0003, validation loss: 0.0021, best val loss: 0.0021\r"
     ]
    }
   ],
   "source": [
    "my_model.fit(\n",
    "    train_transformed,\n",
    "    future_covariates=covariates,\n",
    "    val_series=val_transformed,\n",
    "    val_future_covariates=covariates,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at predictions on the validation set\n",
    "Use the \"current\" model - i.e., the model at the end of the training procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def eval_model(model):\n",
    "    pred_series = model.predict(n=26, future_covariates=covariates)\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    series_transformed.plot(label=\"actual\")\n",
    "    pred_series.plot(label=\"forecast\")\n",
    "    plt.title(\"MAPE: {:.2f}%\".format(mape(pred_series, val_transformed)))\n",
    "    plt.legend()\n",
    "\n",
    "\n",
    "eval_model(my_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the best model obtained over training, according to validation loss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading model_best_290.pth.tar\n"
     ]
    },
    {
     "data": {
      "image/png": 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zZg1ffPEF4NtdiIwKuOEQtPVgsMyCFkII0YlZLBYeeugh2+e+DL0TJ8sgOI7AAN1W+UoFLIQQoktYvnw5W7ZsITTUWPrRV6Gn6zqFpcGAUf0GBFhnQctCHEIIITq7mpoa5s2bB8DcuXMB3wVwWVkZdQGJACTF2zcXsi3EIfcBCyGE6Ky2bt3K/v376d27N/fddx/guwBuuA60KSoCAgMsEBhJ4clyn7TFUySAhRBCuCQvLw+AQYMGER0dTVBQEBUVFT7ZBMHZOtAAiqIQE1EHQEGxxevt8CQJYCGEEC7Jz88HICEhAUVRiI2NBXwzEateBRxX/1hclA7AydJAr7fDkySAhRBCuKSgoAAwAhjwfQAHm0PQSr1jCTFGlBWXSwALIYTohMwATkw0JkOZAeyL68DOFuEw9YgzgresOhSLpeMMQ0sACyGEcEleXj70uIEyZQDg2wBubgg6IcaoiPXAeEpLS73eFk8J8ncDhBBCdAwHjkfCgPk8t7qO08foxMQY9wD5IoBPnDjhMARd/1jDHZGio6PpCKQCFkII4ZKck2EA1FoCufpxnUz9csA3AXz8+HGns6Ch4+6IJAEshBDCJSdKjdWvkuOqAdicdzUk3eiT0MvKLrAuQ2khLqr+sY66I5IEsBBCCJeUVEUCcPX4Kv7vVmvVGXehT0Iv87gR+nGRtbZlKE0JHXQ9aAlgIYQQLdJ1nbJa45pv3/RwzhxkPeCjJSBzCozZzQ2Hn8GxAu5Y60FLAAshhGhRaWkpepBxDTYjOZjEWOuB4ESvB7DFYqGgxNiIIa1747nDjhsySAUshBCiUykoKIAQYy/elARItG2C0N3roVdYWGjbiCElsXFs2TZk6GA7IkkACyGEaFF+fj6EpABGANuuuwYnUujlAM7JyYGQHkDje4Ch/iQsqYCFEEJ0Krl55n24FpLiITREITKsDpQgThTVefXcOTk5EGxUwN1jlUbHI8MhQLFAYDfyT3SchTgkgIUQQrTowNFSUAIIDSghOMgIwfho32yCkJOTA0HG7CvbcLMDRVGICjN2ZMo/6d03A54kASyEEKJFh7ONgIsMKbE9ZlajReUhXj23YwDHRzl/TrR1S8L8It2rbfEkCWAhhBAtysw1Ai42osL2WFK8UflW1HajtrbWa+c2Ati4+BvfxCqT8VHGbUonSztOrLW4FrSqqlHAl8AQYIymadsdjgUBrwL9gR81TbvXWw0VQgjhP9knjGo3MdoetN1jrR8EJ1JUVGTbptDj587OhmDjtZsMYOtylEVlHWdLQlfeKlQA04DlTo5dAmRpmjYWiFBV9WxPNk4IIURjuq5TVVXl03PmFxv34SbH27f7s9+K5N17getdA24igLvHGfVkcXnH2WOoxQDWNK1W07S8Jg6fBayyfrwSkAAWQggv+/3vf09MTAwHDx702TlPloUDkNbdHhuJMeYmCN4N4OycHAg2ArjhOtCm9CSjfaWVwZSXl3utLZ7U1rcKsUCx9eMioNEiYaqq3grcCjBnzhwmT57cxlO2PzU1NWRlZfm7Ge2C9IVB+sFO+sLOE31x/Phx/vWvf1FbW8uXX37J1KlTPdS65hVXRkAIxHWrtH0PgXo4EAvBiezbt4/k5GSXX8+dvjiWUwzxQUSE1pGXm+v0OcFKNyAaguP54Ycf6N+/v8tt8ba0tDSnj7c1gAsBc0AgFjjR8Amapi0EFlo/7TjT09yQlZXVZAd3NdIXBukHO+kLO0/0xb///W/bhKegoCCf9W2lbgTfGUNSSEtLBeDU3jqgQ3AiwcF1brXF1b6orq623eaUGBvQ5Nf0Tre2JSiBysrKDvEz19bpYpuAKdaPLwA2tPH1hBBCNKG6uppXXnnF9rkvl12sUYyFMAb2td+I64v1oHNzc23Dz+ZEK2cSHDZkOHTokFfa4mkuBbCqqiswgvZVVVVvUFV1gfXQJ0C6qqrrgApN077zUjuFEKLL++CDD4wJSVa+CuCysgoIMpaC7JsebnvcF5OwXJmABQ6zo4PiOHz4sFfa4mkuDUFrmtbwIsMi6+O1wGwPt0kIIYQTL730EgADBgxg9+7dPgvgvYcKISAZpa6QsFD7rUa2APbiJKx6i3A0E8D2PYETOkwAd5w7loUQogv78ccf2bhxIzExMdxxxx0APtt4YPdBY65tCAX1Ho+LAgULBMdRUFjs7EvbzLgHuPlVsKD+hgydaghaCCGEf73zzjsAzJ49m9RUYxKUryrgA5nG6lcRgSfrPR4QoBAZVg1AboF31mB2tQJ23B7xkFTAQgghPOXIkSMAjBo1ipgYI218FcCHs42QjQora3QsJqIGgHzvFMAN1oFuehJWRJhCZLgOAaEcO15KdXW1dxrkQRLAQgjRAZj3zKampvo8gI/lG3eQxnerbHTMXIP5RHHT4dgWOQ6LcDjbCclRjzhzYZDuZGZmeqU9niQBLIQQHcCxY8cA/wTw8UIjKhJjGm+4YN6KdLIs2CvndmUnJFOPOOsHIT06xHXgjrNophBCdFG6rtcL4NJSY9N5XwVwQYmx3WCqk70WkuKNcC6pDPXKuXNyciCy5WvAAD1irR8E9+gQM6GlAhZCiHauqKiIiooKoqKiiIqK8nkFXFQRAUDPpMY1W0qiEc7l1eGNjrWVruv1hqBbCuAkczFkCWAhhBCe4Hj9FyA8PJygoCCqqqp8sitSaY0x9ts7NazRsfQeRuVbZYmirs6zM6FLS0uNjRXMrQhbGoKOtX4Q3L1DDEFLAAshRDvnOPwMoCiKz6pgXdepssQC0K9Xt0bHu5sTn4K7U1JS4tFzZ2dnGx8EGRd3WxyCNtsSIhWwEEIID2gYwIDPArisAnQlFOrK6ZnaaMM7ry5HmZOTA4FRoATTLRxCQ5qfaW2bhCVD0EIIITzBHIJ23OHHVwGcb758TT4JCY1nYdkDOMHjAbx161aXZ0BD/SHoo0ePenxI3NMkgIUQop3zZwV8KMuYca3UnSA2NrbR8e7mQx5eD7q8vJxnnnnGpVWwTGYFHBSeSm1tra3f2isJYCGEaIHFYmHdunXcfffdZGRkcP311/v0/P4M4F378wEIDypDURoPATsuAVlYeNJj5/3nP/9JdnY2pww+E3CxAna4Dxho98PQEsBCCNGCyy67jHHjxvHyyy9z5MgRli5disVi8dn5/RnA+48Yrx8V7nxpx4gwCFSqITCc3PxSj5yzuLiYp59+GoCZ19wGtLwKFhg7IikK1CoxQKAEsBBCdGR1dXV88cUXADz44INERRm32xQUFLTwlZ7jz2vAh48Z6z+bS042pCgK4UHGc7LyPHNL1AsvvMCJEycYO3Ys6b2HWc/f8tcFBirWijygQ+yKJAEshBDNOHbsGNXV1SQnJ/Pss8+Snp4OWGfo+oDFYrHdjpOSkmJ73FcBfCzPqHx7xDUdF1FhRvAezalo8/kqKip44YUXAHjqqacoLDGGvV25Bgwdaya0BLAQQjTjwIEDAPTt2xeA5ORkwHcBnJeXR11dHQkJCYSG2pd79FUA5xUalW9aj6aXmoyLMmYbH81uewDv2bOHkpISBgwYwNixYzlRbN0IItq1zR5sM6FDevjs36i1JICFEKIZ+/fvB+wBbFahvvrlbl7/dRx+Bt8FcGFpIAC90yKbfE6PWCNKcgoab9bgrn379gFw6qmnAlBg3ebQlSFoqF8B5+Xltbk93iQBLIQQzfB3BdxwGUqTrwLY3GShf++4Jp+T2t1YDzq/qO1bEpoB3L9/fwBOWAPYlUlY4BjA3cnNzW1ze7xJdkMSQohmmAHcr18/wB7AtmUSvczZDGjwTQDruk5VnVF6Duqf2OTzeqcZmzWcLAtp8zkbBbB1dUuXK+BYBdAhOMntCnjtFp0f98CEM+CMAd7Z39iRVMBCCNEMf1fA/gzg/Px8dOtCGL1SIpp8Xp90YyekKj2OsrKyNp1z7969QOMK2N1JWEpYMiUlJVRWVrp87v+u13lwvs43P7v8JW0iASyEEM1oeA3YXwHc8BqwuSqVNwP48OEjEGxUvgnNBGBqgrkhQ3KbRwYaVsAFrQzgkG7GbHV3quDjhdbXiHX5S9pEAlgIIZpQXFxMfn4+YWFhtuDtSteAd+/LBiWIIMqb3Qgh2dyjISSpTcs/lpeXk5WVRXBwML169ULXdXsF7PIQtPF3YKjx79SaAE5qvOeEV0gACyFEEw4ePAgY1W9AgPHrsisNQe8+eAKA8ODyZp+XbO7REJJie8PQGuZwf58+fQgKCqK0AmrrjNW2wkJdvA3JWgHrQUbl7s5ErFwzgJueb+ZREsBCCNGEhtd/ARISEggKCqKwsJCqKs+s/NScpgI4PDycoKAgKisrqa52vkxkWx2wLkMZHdH86xtVp8XYhSiz9UPQTc2AdrX6BXsA1yjGB60agpYAFkII/2p4/RcgICCApKQkAI4fP+7V81dXV5Obm1vvnCZFUbxeBR+xLqyREK03+7ygIIXIkApQAth/pLjV52vrBCyAqAgIDYFaPQwCIlwO4JpanYIiCAhw2GDCyySAhRCiCc4qYPDdMLT5+snJyQQGBjY67u0Azs43Kt+k+JbvWI2PNJ57+Jjrs44bausELDDemDjuC+zqEHTeSePvxBhjTWlfkAAWQogm+DuAmxp+NpkB7Ml9eB3lnTQq357JYS0+t0ec8dzMvLpWn88M4FNOOQWAw9bubW4GtjO2SVRurIaV6+MZ0CABLIQQTWq4CIfJVwFs3tJjnq8hb1bAdXV1FJcbC2tkpDa9DKUprYdRJecWtj5WGlbAS9cYoT5Jda8idVwP2rEC/vLLL1m5cqXTNyy+ngENEsBCCOFUXV2dbTu73r171zvmqwA2q7cePXo4Pe7NAM7OzrYtwpGS2PIQdN80o0ouLAtF15u/ZuxMZWUlR48eJTAwkIyMDA7n6Kz90biee9UE916rqfWgH3nkES666CJ++umnRl9z3Jjw7bMZ0ODiUpSqqv4VGAMcAW7UNK3a+ng48B4QBdQAV2qadsJLbRVCCJ/JzMykpqaGlJQUIiLqrwLlq+UozfDo3r270+PeDOCjR4+C9VYeVyYl9UoOBXRqlQROnjxJXJx7SXbw4EF0Xad3794EBwfz1hc6ug4zxkJcVCsr4AbXgM1JdQ1HNMDhFqT2VAGrqjoCSNE0bSywA5jpcPgiYLumaecB7wDXe6WVQgjhY01d/4WuUQEfOXIEgo0bfF0JYMd7gVuzGIfjDGhd13ljpVFFz77I/QlRPeLMlbnsFXBJSQl5eXmEhoY2WlUM4HihcT5jLWnfcGUI+ixglfXjlcDZDsf2AuZbw1igfe/9JIQQLmougH21JaHfK+Bg1yvgFFsAt241rBc/7gFDPiU2bRzrt8H+LEjrDpNGuv1SDutBp1JaWkpFRYWt+u3Tp49tURVH/rgG7MoQdCxg9mYR4Ni8/cBpqqpuB3TgzIZfrKrqrcCtAHPmzGHy5MltaW+7VFNT06bVXzoT6QuD9INdR+2Ln382VuTv3r17k+3Pyspy63tzty+OHj0KGLfWNPd17rbDFTt37oTgGwCoqcghK8vS7POVmkCgB4Qk8+uvnzN48OBmn9+wL77ZOwziw3hv3xR++L8aIIgZZ5eSk1PidttDCQESCIroRQ3wyy+/2K77pqWlOe2rI9lxQBiBlgKysjy7wIqzihtcC+BCwJwEHgs4XuOdBXytadoTqqpeDjwKzHX8Yk3TFgILrZ+6f2W+A8jKymqyg7sa6QuD9INdR+2LgoICAIYNG9ao/WblmZeXR2pqKori2rClu31RXGzcCDto0CCnX9erVy/AmDDm6T4+caIIguJR0Bl8SjJBQc1/j5ExOsY2gMmUl5e32B7Hvigp16nVddAt6ARwINs4191XRpGW5uY9SMDpFqMtSlhPAAIDA20zn0877TR6JKUy9Q86ldXw9T8UAgMViiqMNxiD+yeQltZ+7gPeBEyxfnwBsKHBcTOQT2IEtBBCdHjm8LI53OwoMjKSyMhIKisrbSHpDf4cgt65LweUAKIi6loMX4DobhAcUAtBURw6mt/sc4uKiurNlM4psH5QeYilczM5fwTMuRxO7dm6IEy3dldNgHHtPDc3t94ErOeWwZcarN8Ge4xBBr/Mgm4xgDVN2wpkq6q6DhgMvK+q6gLr4cXARaqqfg08AbzgrYYKIYQvmctMNlwC0uTtiVi6rvstgAsKCti93wjRpPjGK3A5oygKcZHG0O2hZlbD+uSTT4iPj2fBggW2x/YeMfYQVmqPc/nkVL76RwAv/b71d8lGhCnER4NOCAR3Jy8vzxbAobHDePwNe/j/uAcsFp3ck8bnvloHGly8DUnTtAcbPHSb9fFi4GJPN0oIIUwHDx4kNTWV0NBQn57XvH2lqRnIycnJ7Nu3j5ycHAYMGODx8xcXF1NTU0O3bt0IDw93+hxvBfCGDRtsE7C6uzEruEeshdxiyDxe4/S4xWLhj3/8IxaLhc8++4wnn3wSgM0/HgIGExtRSXBwcFubDxhV8IliICTdoQJWePWbkVRVGxV7cRn8uEfnwjMV6uogJpJmt130NFmIQwjRbu3Zs4f+/ftz3nnneW3HH2dqa2vJz89HUZQmq09vV8AtVb/gvQD+9ttv3ZoBbUrtblTLx5tYDeuDDz7g119/BWD79u1UVBibPfy087j16z0XSeYwNKHpHDt2zLitKvlmtD0R9IiDf95nBO2Pex1mQPuw+gUJYCFEO7Z582YsFgubN29m7ty5LX+Bh+Tn56Prum3rQWc6cwCvW7fOrUU4TL1TjVGKwrJQLJb6s6YtFgtPPPEEYOwoVVNTg6ZpAOw9bMx0PqVXy0teusoewGn88MMPWCwWgjPuBuBvcxQmq8bhH/fYr0FLAAshhNWePXtsH//tb3/j448/9sl5Wxp+Bu/fC+xKG7wRwKWlpWzZsgUl1EgwdwI43boetB7UeBOEjz76iF9++YX09HRmz54NwMaNGwHIyjU2cBg2sOnv1V3pPaxDySHptqCvCzHu6Z46BpLiFVITjWHo74yi3KfXf0ECWAjRjpkBfOaZxhIDs2fPNoYSvaylCVjg/eUoXamAIyIiCAwMpLKystVD9JmZmaiqyttvvw3Apk2bqKuro0eqcR9vohvXgJPNVSJCksnMzLQ9ruu6rfqdO3cu48ePB4xrzcXFxRRXdgPgjKHOd31qDcch6MrKSghJxUI4iTEQa13a8oxTjad8vtmYlCUVsBBCWJnLE/7tb39j6tSpFBYW8re//c3r5+0oAawoSpur4JUrV7JlyxZuv/12srKyjOFnICHZSCd3KmB7AKfYNrIA+OGHH/j5559JSUnhd7/7Heeccw5gVMA//vgjBBv93LOHS/OCXdLTLKZD042/w43v55R0+3PMADYr4KR4303AAglgIUQ7peu6rQIeMGAAt956K2APZW8yh3+bC2BvD0G7EsBgH6Ju7RsB83stKyvjD3/4gzEBCwiPNhaxaF0AJ9mW8gTYvXs3AOPGjSMsLIzevXvTo0cPCgoKWLZsGYQYfWlbztIDHCtgAMKNPYZP7Wl/zhmnGIFrXq6WIWghhMAIlLKyMhISEoiPj7et+uTLIWhXrgG3Zt1jV7gawH369AGoV3G25jwAS5YsYf369YBCXplxXneGZW0BGpxcL4AbrqutKAqqasyCWvTWUghOREGne2yrvgWn0hoFsLHH8Cnp9irXrIBNMgQthBDYr/+eeqrxW9IM4MOHD3v93K4MQXfv3p3AwEDy8/O9couUqwFs7lV88ODBVp3HrIDNtZtra2vpOewWjuQG0iupcUg1x1ZBhvRg//5DtsedbWxhBnBFbSQoAcRH1bq04paroiIUYiKBgHAIiocwowJ2HIJO71G/wpcAFkII7EPNZgDHx8fTrVs3iouLvbL0oiNXhqADAwO9eiuSuxVwawPYPM+f//xn22uROgeAu2YoboViSLBCbLdaUILYd+Sk7XEzgB334R050rrNUYjRh+kevP5rchyGDowaCNQPYEVR6r3BkCFoIYSgcQWsKIrPhqFdGYIGSE01Zu16YxjaV0PQ5puNXr168eabbzLi7Ks4WjaEsBD4XSvWOUxJNGLlaE4NdXXG7UXmMpB9+/ZlxyGd8++xcJJRxupm5vXfRM9PgLIHcAaWEKOfHAMYYKTDIma+3IoQJICFEO2UGcCnnHKK7TFfB3BzFTB4L4B1XbcFo7eHoLMLQ2HED7z6ZX9GjjqXsVcuAeDayZAQ434oZiQbsVIbmEJWVhbl5eVkZ2cTFBRESko6s/5P55uf4I0vYxg1ahQEGxWwJydgmWwBHD0GXQkhJQEiI+p/T2ecanweHgqRzlf89BrP1/xCCOEBDStg8M11YMfwa6kC9tZErNLSUqqqqggPD6dbt27NPtdxCFrXdZe3RgTjey2oGwGRZ7DgM/jyJ51c67KMd1/Ruoo0w3zPEtqLAwcOUFpaChhvFBZ8EoC2y7jn9se9IcybfinrXzoJOMyg9iBbAMcY9x03rH4BRg8CRYE+KbjVd54gASyEaHfq6upsw5b9+/e3Pe6LCvjkyZPU1NQQFRXV5CYIJrMC9vS9wI7Dzy2FQnx8PFFRUZSUlHDixAkSElwvJYuKiqgLNN5kBAXCfus+9WOHwfD+rQzgZAXQISyDgwcP2gI4tc9o/vdVI3xDgqGoLICpV9zHz6XFLPkKUhI8H349exhtUaJHoeM8gHslKXzxHKQmevz0LZIAFkK0O4cPH6ampob09PR6FWBGRgbg3QB2dfgZvDcE7er1XzCqtj59+rBt2zYOHTrkVgDn5eXZrsE+e4dCcRm8s1bnL7e1PgztFXAGBw5sp6TEWOf5aMj9lBTDZWONsF/+NWzeEUBFrTEN2StD0NYBDN16tbWp/YUnj/Jt5WuSa8BCiHbH2fVf8E0F7MoMaFN7CGBo/XXg3NxcWwBnJMFjNyrseCuAc4a2IYCTrR9Yh6D3798P3YZxsPh0IsPhxXsUzj7NeP3vftXJPmE83atD0FbOKmB/kgpYCNHuOLv+C74JYFdnQEP7CeDW3opkVMCenQRlq4DDMjhw4ADFxcXQbRgAF42BnkkKZw0xhqI3boeKKjx6fkftPYClAhZCtDsN7wE2paWloSgKWVlZ1NQ43/S9rdwZgvbWJKzWBrC7tyI5VsCeCsCUBAgK1CEkmQOHjhn3AFuXgRxgXQZyxCkQGqyz8zBkWhfi8kYFHN2t/szmfmmeP0dbSAALIdqdpoagQ0JCSElJwWKxeG0JSHeGoBMTEwkKCuLEiRNUVVV5rA2+GoI+nptnuw3IUwEYGKiQ3t0YYs4tCmXfvn22jRDMa7ChIQpD+xhvoGrrICoCuoV7/jqsoii2KrhXEoSH+udab1MkgIUQ7U5TQ9Dg/YlY7gxBBwQE2KpgT86EdvUeYFNrh6CPHCuDgGDCgysJ82A42a8DZ1BdXU1g1CCg/kYII0+xL9/pjerXZE7Eam/DzyABLIRoZ2pqajhy5Ihtdm9D3r4O7M4QNHjnOrBZAbvyJgDsFfChQ4fQdd3l82TmGitVxXerdK+BLXCcCQ2gh5obIdifM/JU+yUEb1z/NZkVsASwEEK0ICcnB4vFQnJyMiEhIY2OezuA3RmCBu9cB3Z3CDo6Opr4+HgqKyttbyBccbzQqHq7x9a538hm2CrgsF4QkopFCScxBuKj7VW2YwXszQA2V7o6a0j7Gn4GCWAhRDuTlWWsBpGW5nzGjLdXw3JnCBq8WwG7GsDQumHovCLjRphUD6/DnJFkfb3QDIfrv/WfkxRnobc1qL05BH3XDNi9WOH6C7x3jtaSABZCtCuZmZlAywHc3oagPXkN2FcBXFRhTBHumRTsRutaVq8CbiKAAc46zfjb028AHAUEKJzaU/H5MpOukAAWQjj1wQcfMGnSJI8vs9iSlipgb07CKisro6ysjNDQUKKjo136Gk9XwOXl5ZSXlxMSEkJUVJTLX+furUi6rlNabXyPfdM9uwtBvWvA1luQnK1C9cgNCtdNgVkXevT0HYYEsBDCqfnz57NmzRrmz5/v0/O6MwTtzoQjVzhuwuBqxdSWAF60aBFnnXUWR48etT22b98+wKjA3ana3L0V6eTJk+hBRlL2SvZsBdzTHL0PTYcI6wxoJ5OgBvVWeOvhAJLi21916gsSwEIIp8wKc8mSJR4Puua0FMCxsbFERkZSWlpKUVGRR8/t7vAztG0S1r///W82bdrEc889Z3vs7bffBuCCC9y7aOnKEPTzzz/P8OHDyc7Oti7C4Z2tAMNCFeO6rhJEQNxYwPkQdFcnASyEaETXdVsA79+/H03TfHbulgJYURSPTMTSdR2LxVLvMXdnQEPbKmCz/f/5z38oKSmhpqaGRYsWAXDTTTe59VotDUHrus4LL7zAtm3bWLRoUb2NGLwxC9m8DmxRIgHo3w5vA/I3CWAhRCP5+flUVtrvDV2yZInPzt1SAEPbJ2LV1dUxatQozjnnHMrKymyPuzsDGiAhIYHg4GBOnjxJRUWFy19XW1tr+15LSkp46623+Pzzzzl+/DgDBw5kzJgxLr8W2Pvk6NGjjd5YgLG4ifkmYfny5Rw/7vllKB1lOLyH6dmj/a1C1R5IAAshGjGDLSIiAoB33nmHujrP3ivqjK7rtlBKT2+6ZGrrRKycnBy2bNnCpk2buOeeewCoqKhgwYIFgP16qisURWnVTOisrKx6ffryyy/z2muvAUb16+6s3YiICLp37051dTU5OTmNjq9du9b2saZprP/uZwiMIEipJCrC8+FomwmNDD83RQJYCNGIGWwTJkygb9++ZGdn880333j9vIWFhVRUVBAVFdXsDOC2VsCOQfn666/z9ttvc9NNN7Flyxb69OnDXXfd5dbrtWYY2hwqVlWV1NRUdu7cyccff0xgYCA33HCDW+c3mW9MnA3NmwFs7q/85rLVAESFlrbqXC3p1cMe6hLAzrkUwKqq/lVV1XWqqi5WVTWkwbGrVVVdq6rqt6qqjvZOM4UQvmQGW0ZGBtdccw0AS5cu9fp5XRl+hrYvxmEGsHmr0axZs1i2bBmRkZF8/PHHbm1qD62biGW2vX///tx+++22x6dNm+bWNWhHTQWwxWLhq6++AuCRRx4BoLA0FIC4bp7bRKJeWxwr4HQZfnamxQBWVXUEkKJp2lhgBzDT4VgqcCkwUdO0cZqmfe+1lgohfMYM4F69etkCePny5R7d8ccZdwO4rRXwFVdcwTXXXIPFYkFRFBYvXsxpp53m9uu1pgI2Q7J3797ccsstBAcbtwK5O/nKUVMBvG3bNgoKCujZsyd33323cWnBev23e0xtq8/XbFtkCLpFrlTAZwGrrB+vBM52OHYhUAWsVlX1LVVVIz3cPiGEH5i/wHv16sWQIUMYMGAAJ0+e5Oeff/bqeX0dwCkpKbzyyivceuutLFq0iOnTp7fq9czr1e60x+zjjIwMkpOTeemll7jnnnuYOnVqq9pgvpbja5vM4ecJEyYQERHBxRdfbL8FyUurUDlOwpIAdi7IhefEAubbuiLAcdXOJOvxycDtwBzgaccvVlX1VuBWgDlz5jB58uQ2Nbg9qqmpsf3i6OqkLwwdvR/MxSDCw8PJyspi4MCB7N69m/Xr17cYjg250xc7d+4EjKHhlr4mICCAY8eOcejQIVv16CrH76+kpIRHH30UoNX/ZnFxcQBs37692ddw7Atzy8Vu3bqRlZXFtGnTmDZtmlubKTQUGWnUQLt3767XjhUrVgAwYsQIsrKymDhxIu9pxQDEd6vy2s/qoF6JlFYoBFvyaHiKjv5/xB1N/Z9xJYALAXNNtljghMOxk8BXmqbpqqquBf634RdrmrYQWGj91Hd38/tQVlaW27+UOivpC0NH7wdzFu3IkSNJS0tj1KhR/Pe//+XYsWNuf1/u9EVJSQkAAwcObPFrUlNTW1w3uinFxUb4DBo0yCP/TmeeeSZgrGPt7PV0XWf9+vXous64ceMAexVu9rEnnHHGGYBxO5X5mjU1NWzevBmAyy+/nLS0NK677jruevET6oDhg5NIS4v1yPkb2vq6jkWH8NDG319H/z/iCa4MQW8Cplg/vgDY4HBsA3C69eMRwAGPtUwI4RfmlnaBgYG2yUVDhgwBjArPm1wdgoa2TcRyHIL2hH79+gHGoiUNb9c6fvw4V1xxBePGjeOqq67CYrFgsVjqTXTzFMchaHP1si1btlBaWsopp5xCz57GWHC3bt0YPGIiAIP6xnjs/A2Fhihy/28zWgxgTdO2Atmqqq4DBgPvq6q6wHpsm/XY18As4EUvtlUI4QNmVZmenk5gYCBgD+Bff/3VZ+duSVuuA3s6gCMjI0lOTqa6urresOrKlSs57bTT+PDDDwFjZGHr1q0cP36c6upqEhMTbbcFeUJsbCxRUVGUlpZSWFgI2K//Tpw4sd5zqzFmentzL17RPFeGoNE07cEGD93mcGyeR1skhPArxxnQpr59+xIWFkZmZiYnT54kNjbWK+duTQXsbgBbLBbbELunAhiM24lycnLYt28fvXr1Qtd1rr/+evLz85k0aRIRERF8/PHHrF69mvPPPx/wbPULxqIgGRkZbN++ncOHDxMfH8/GjRsBbEPfpuwC428JYP+RhTiEEPU4C+DAwEAGDTJ2tdmxY4dXzltVVUV+fj6BgYEuLQXZ2tWwCgoKqK2tJTY2lrCwsFa11ZlTTjG23du7dy9gLLSRn59Pjx49WLVqFddeey0Aq1evrjcD2tMaDkOb13/N69SmNX9T+PgvCvGu7boovEACWAhRj7MABmz3x3rrOrB5D21KSopt6Ls5ra2APT38bOrfvz9gn2Ft3rJ1+umnoygKEydORFEU1q9fb5vt7e0ANt8EJCYm2jZrMKkDFS45p31uVN9VSAALIeppKoC9fR3YneFnaP0kLF8H8PDhwwFj04ahQ4dSXV1t23LQnTWnXeUYwGb1O3r0aAnadsila8BCiK7DXxVwawP4yJEj6LrucsD4KoB/+uknwKiATWPHjmXbtm3s378f8H4FbGo4/CzaB6mAhRD1dJQKOCYmhqioKMrKymwzfl3h7QDev38/FoulUQUMjSdCeTuAv//eWB149GhZpr89kgAWQtjout5kAPfq1Ytu3bpx/Phx8vPzPX5udxfVMGf8gnvXgb0VwNHR0fTo0YOKigp27tzJwYMHCQ0NZcCAAbbnqKpKeHi47XNvBvCBAwfYsmULIAHcXkkACyFsCgoKqKioIDY21rZTkCkgIMCrVbAr+wA31JrrwN64BclkVsEffPABYAzbBwXZr/SFhobaquCoqCiv3M6VlJRESEgIhYWFVFZW0r9/f+Lj41v+QuFzEsBCCJumql+TJwLYnIR09OhR22OVlZW213RnecLWzIT2VgUM9gBevnw5UH/42TRlirGwYO/evb0yMSogIKDev59c/22/JICFEDYtBbAnJmK98847XH/99QwZMoTXXnuNgoICJk+ezK+//kpcXJzT0GqKKwFcVlbG/PnzbcPmvgjgbdu2AfUnYJlmzpxJUlJSq3decoXj0LYMP7dfMgtaCGFjDuWaawY35IkK2PzakpISbr75Zn7/+99TWlpKeno6K1assO0s5ApXAvjOO+9k0aJFrFu3jsWLF3s1gM3FOEzO3kz06tXLNgzuLY4BLBVw+yUVsBDC5uDBg4Cx9KQzjhWwudi/u8zbdH77298SFxdHaWkpw4cPZ9OmTQwdOtSt12ppEtZnn33GokWLAHjvvffYs2cP5eXlREREEBUV1ar2N8esgE3uVPOeZPZLcHCw39ogWiYBLISwOXDA2NCsqQBOTU0lOjqaEydOtHomtHkP7L333suvv/7KG2+8wbp161q1NV1zk7CKioq47TZj2frExERqa2t5/PHHAaP69cb1V3NXJDCu8cbEeG+noeaYAXz66ad7dLlN4VkSwEIIGzOAGy5baFIUhVNPPRUwNn13l67rtgq4X79+pKSkMGvWrFZXo6mpqQQEBJCdnU1VVVW9Y/fffz9ZWVmMGTOGZcuWAbB06VLAO8PPAHFxcSQkGLsbOLv+6yvTpk1j/PjxPPhgw310RHsiASyEAIxwbKkCBmwBvGfPHrfPkZeXR2lpKbGxsR65NSYoKMhWOTvOqt62bRuvvfYaoaGhvP7660yYMME2fA7eC2CwD0P7c+g3ISGBtWvX8pvf/MZvbRAtkwAWoh06cuSIbXMCXzl+/DgVFRXEx8c3O3RqLizRmgrYrH779+/vsSFg8w3Brl27bI+ZayDPnDmTQYMGoSgKc+bMsR33ZgCPHTsWgEmTJnntHKJzkAAWop2prq5m5MiRDB06tN7m7t7W0gQskycC2PFaaVs5m5ltbpnoWPVed911toUvvBnATz75JPv27ePcc8/12jlE5yABLEQ7s3//fvLz8zlx4gS33357q2cbu8uV4WdoWwCbE7AazhZuCzOAHe9NNgPYPAbQrVs3/ud//geAs88+22PnbygsLMyjbzBE5yUBLEQ74xhsn376KUuWLPHJeVuagGUy73Xdv38/tbW1bp3DGxWwWeU6q4AHDx5c77kPP/wweXl5jTZFEMIfJICFaGfMADb3ir3nnnu8vnADuF4Bd+vWjfT0dGpqajh06JBb5/BGBWyG7M6dO6mrq6O4uJjMzEzCwsIa7berKAqJiYkeO7cQbSEBLEQ7Ywbwgw8+yJQpUzhx4gR/+MMfvH5eV68BQ+uHob1RAcfGxpKenk5lZSUHDhxg586dAAwcOJDAwECPnUcIT5MAFqKdMW/vGTBgAH//+98B+OKLL7x+XlcrYLAHsDu3Ip08eZKCggLCw8M9PgnKcSJWU8PPQrQ3EsBCtDNmVTlgwAAGDBhAZGQkubm55Obmeu2cVVVVZGZmEhAQ0OQ60I5asxiHOfzcr18/j69C5bhEpgSw6CgkgIVoR8wlHiMiIkhLSyMgIMAjOxC15PDhw+i6Tq9evQgODm7x+a0ZgvbG9V+TVMCiI5IAFqIdMQPt1FNPtVWJ5gYFv/zyi9fO6871X3A9gF999VWeeuopqqur6y3C4WmOtyJJAIuOQrYjFKIdcbz+a/JFALtz/ReMTRBCQ0PJzs6mpKTE6VrOTzzxBI899hgAFRUVtjcU3rhH1gzbXbt2UVtbS3BwsNyLK9o9qYCFaEccr/+azAD25hC0q/cAmwIDA22VrLOJWE899RSPPfYYAQEBhIaG8sYbb9g2QvBGBRwZGUnv3r1t9yUPGDCAoCCpL0T7JgEsRDviLIAdrwFbLBavnNfdChiaHoZ+9dVXefjhh1EUhTfffJOXXnoJgMrKSsA7FTDUX3ZShp9FRyABLEQ7YlaT5ixjMPayTU5OpqyszO2FL1zVlgBuWAEvWrQIgBdffJHrrruOqVOn8vTTTwMQGhrq0izr1nBcdlICWHQEMkYjRDtRV1fH3r17gfoBDMYwdE5ODtu3b3crJF3h6jaEDTV1K5I52Wr69Om2x/7whz8QFRVFQkKC14aGpQIWHY1UwEI04a233iI9PZ0NGzb45HxHjhyhqqqKlJQUoqOj6x3z5kSswsJCiouLiYyMtG0m74qBAwcC9bcBLC0tJScnh9DQUNLT022PK4rCnXfeyVVXXeW5hjcgFbDoaKQCFsKJn3/+mVtuuYWqqiref/99zjnnHK+f09n1X5NZ3XkqgI8ePcr777/Ppk2b+OmnnwCj+nVngYxBgwYBRgDX1dURGBhou9e3b9++BAT49v39wIEDiYiIICAgwLZhhBDtmUsBrKrqX4ExwBHgRk3Tqhsc/yNwhaZpquebKIRvlZaWcuWVV1JVVQV4d/axI2fXf02emgn9xRdf8MQTT7Bx48Z6jwcEBHD11Ve79VoxMTGkpKSQnZ3N4cOH6du3r1fv9W1JeHg4q1atQlEUQkJCfH5+IdzV4ltUVVVHACmapo0FdgAzGxyPAk5z9rVCdDS6rnPHHXewZ88e22Qhx23uvKm5Cnjw4MEoisLu3buprq5udNxV99xzDxs3biQ8PJyZM2fy+uuvo2kaJSUl/PGPf3T79cwq2NwAwZurXbninHPO8epev0J4kitjRGcBq6wfrwQa/nTfC/zTk40Swl+++OIL3n77bSIiIvj888+JiIjg2LFjFBYWev3czQVwREQE/fv3p7a2tt41V3dUVVWxb98+AgICyM7O5r333uPGG29k5MiRREREtOo1GwawPytgIToaV4agY4Fj1o+LgHjzgKqqMcBQTdP+rKrOR59VVb0VuBVgzpw5TJ48uS3tbZdqamrIysrydzPahY7eF4sXLwbgtttuIzY2lv79+7Nt2za+/vprRo8e7fLruNsPuq6zZcsWwLjtyNnX9uvXj7179/Ltt9+6NVnKtGvXLiwWC3369KG0tJTS0lK3X6Mhc1cjTdPIysqyjRbExMTYvoeO/jPhSdIXdl2pL9LS0pw+7koAFwLmlMxY4ITDsd8DLzf3xZqmLQQWWj/VXThfh5OVldVkB3c1HbkvdF1n/fr1AFxzzTWkpaUxYsQItm3bRm5urlvfl7v9cPjwYU6ePElCQgKjR492Ohlq9OjRrFy5stV9/N133wHGhC5P/RudddZZgDGDOy0tjaNHjwIwZswY2zk68s+Ep0lf2ElfuDYEvQmYYv34AsDxnoz+wP+qqroSOEVV1bkebp8QPrN//34OHTpEfHw8Z5xxBlB/kX9vMqvfkSNHNjkTediwYYAxQ7s1HDeq9xTHIeiKigqOHj1KUFAQGRkZHjuHEJ1ViwGsadpWIFtV1XXAYOB9VVUXWI9dr2nahZqmXQjs1TTtae82VwjvWbXKmOowadIkAgMDgfrb3HnTjz/+CGALfmdOP/10ALZu3dqqc5jXjs3Q9ITk5GRiYmI4efIkmzZtAqB3796yDrMQLnDpf4mmaQ82eOg2J8+RW5BEh2YG8AUXXGB7zBd78UL9Crgpffr0ITo6mpycHHJyckhOTnbrHGYAe7ICVhSFQYMGsWnTJj755BNAJmAJ4SpZCUsIjAkha9euBag3UbBnz55ERUWRl5dHXl6eV87tOAGruQAOCAhodRVssVi8EsBgr6glgIVwjwSwEMDmzZspKSlh0KBB9TYLUBTF68PQWVlZ5OXlERcXR+/evZt97ogRIwD3AzgzM5Py8nKSkpKIi4trbVOdMpd9lFuQhHCPBLAQ2Iefp0yZ0uiYtydimdXvGWec0eJSkK0NYG9Vv9D4mrIEsBCukQAWAtcC2FsVsCvDz6bWBrA3ZkCbGgawt/b7FaKzkQAW7dI777zDs88+65Nz5ebm8sMPPxAcHMx5553X6Li3J2K5MgPaNGjQIEJDQ9m/fz9FRUVNPq+6upqHHnqITz/9FPDODGhTRkYGYWFhgDFk36dPH4+fQ4jOSAJYtDtHjx7l+uuv56GHHmr1sovu+Pe//43FYuHCCy+kW7dujY47VsC67vm1ZNypgIODg21vCJq7H/iNN97g2Wef5corr2Tfvn1erYADAwNty2f26tWL0NBQj59DiM5IAli0O88++yw1NTVA6xedcFVtbS3z588HjKVSnUlJSSE2NpbCwkKys7M9ev7s7GxycnKIiYlxeei2pWFoi8XC888/D0BFRQW/+93vvBrAYK+s5fqvEK6TABbtSnZ2Nq+++qrt823btrX5NX/++WcmTJjAVVddxRNPPMGqVatslexHH31EZmYmAwYMYNKkSU6/XlEUj+zH+9NPPzW6lcmsfkeMGOHyXrwtBfDHH39s280pKSmJb7/9ltzcXCIiIurN8PYkcya07MMrhOtkuRrRrjz//PNUVVWRkJBAQUGBRwL4tdde46uvvqr32IMPPsgzzzzDyy8bS5nfddddzW4gP3LkSNavX8/3339fb6EOVx09ehRVVUlNTWXr1q22zRTMfXldGX42mQFsXjtu6K9//SsA999/Pz179uSKK64AjF2Wmvse2+KOO+4gKyuL+++/3yuvL0SnpOu6L/90SpmZmf5uQrvRlr7Izc3VIyIidEBfsmSJDui9evVqc5suvvhiHdDnzJmj33fffXpQUJAO6LNmzdIBPTIyUi8qKmr2NZYtW6YD+kUXXeTSORv2w8qVK3WMzUj06dOn6xaLRd+4caMeEhKiA/rnn3/u8vdTWlqqK4qiBwYG6hUVFfWObdiwQQf0uLg4vaSkRNd1Xf/Nb36jA/p1113n8jk8Sf5/2Elf2HWxvnCaiRLAHtDFfpCa1Za+mDdvng7oU6dO1Wtra/WwsDAd0AsLC9vUpoEDB+qA/vPPP+u6ruvvvvuuHhAQYAvEOXPmtPgahw4dsgVbXV1di89v2A+vvvqq7XyAPnfuXD0pKUkH9LvuuqvV39MPP/xQ7/HLLrtMB/R58+bZHissLNQfffRRfffu3W6fxxPk/4ed9IVdF+sLCWBv6WI/SM1qbV+cOHFCj4qK0gH9u+++03Vd11VV1QH922+/bXV76urq9NDQUB3Qi4uLbY8vWrRIVxRFB/SdO3e2+DoWi0VPSUnRAX3Xrl0tPr9hPzz66KM6oI8aNapeEI8fP16vrq52+/u65pprdEB/5ZVXbI/l5+friqLowcHBenZ2ttuv6S3y/8NO+sKui/WF00yUSViiXXjxxRcpKSlh0qRJjBkzBrBvv9eW68DZ2dlUVVXRo0cPoqKibI9ff/31rFixgg8++MClmcGKotj2vjX31XWHuU/uzTffbJtt3bdvX9577z2Cg4Pdfj2zj8xryGAsp6nrOmeeeabbGzUIIXxPAlj4XXFxMf/4xz8AePjhh22PeyKA9+/fDxhh19CFF17IjBkzXH4tM/TMbffcceTIEcC4T/aFF15gyZIlrF+/3jYZy13nnHMOUD+AzTcG5hsFIUT7JrOghd/961//orCwkLFjx9ZbicoTAXzgwAHAeQC7yxMVcM+ePQkODuaaa65pU1uGDRtGREQE+/btIzc3lx49etjeGEgAC9ExSAUs/KqsrMy2aIRj9QswdOhQwLj31mKxtOr1zQD2xPrEI0eOJCgoiO3bt1NSUuLy1+m6Xi+APSE4OJgzzzwTMKrguro6Nm/eDNgrdSFE+yYBLPxqyZIl5OfnM3r06Hr78AIkJiaSmppKWVkZBw8ebNXrNzcE7a7w8HBOP/10LBYLP/zwg8tfd+LECSoqKoiOjiY6OrrN7TCdffbZgBHAO3bsoKSkhIyMDFJSUjx2DiGE90gAC78yg+yaa65xuhJUW4ehPTkEDfbhXXeuA5vXfz29CpV5HXjDhg229kj1K0THIQEs/Mrc4s8cbm7IUwHsqS3yWnMd2NPDzw3bomkaX3/9db3HhBDtnwSw8Btd121b/Jk7DjVkBnBrNmUoLS0lNzeX0NBQjw3LmhXmd9995/LOSGYA9+rVyyNtMMXGxjJkyBCqq6tZvnx5vfYJIdo/CWDhN1lZWRQXF5OQkEBSUpLT57QlgM3qt0+fPh5bA7l37950796dgoICDh8+7NLXeKsCBvswdHV1NaGhobZ1ooUQ7Z8EsPAbc/h5yJAhTe4ENHDgQKKiojhw4IAtUF3l6eFnMBbkaGk3ooa8dQ0Y7BOxwJilHRIS4vFzCCG8QwJY+I05/Gxu9edMcHAwF198MWBsHegOT0/AMrUUwFu2bGHBggWUl5cDvqmAQYafhehoJICF3zhWwM0xV6v68MMP3Xp9T96C5KipAN66dSvTp09HVVWefPJJnnrqKcC7AdyvXz+6d+8OyAQsIToaCWABQGFhITt27GDHjh3s2bOn1QtfuKOlCVimCy+8kJCQEDZs2MDx48ddfn1vDEGD8wBet24dqqryySef2IaBFy1aRE1NDVlZWQCkp6d7tB1gDIk/9NBDnHfeeUyZMsXjry+E8B4JYMHx48fp1asXQ4YMYciQIQwYMIB7773Xq+e0WCzs2LEDaDmAo6OjmTRpErqu8/HHH7t8Dm8NQffv35/IyEiysrLIzc0F4K233sJisTBz5kwOHz5Mr169yMzMZOnSpdTW1tK9e3fCw8M92g7T/fffz9dff+3RRT6EEN4nASxYsWIFpaWlxMbG2nYGev311ykqKvLaOQ8fPkxZWRlJSUkkJia2+PyWhqHr6ur46quvuPPOO5k0aRKTJk2yDUH36dPHcw0HAgICGD58OGBUwbqus2rVKgDmzp1LcnIyV1xxBQD/93//B3hn+FkI0bFJAAtbeDz55JPs3LmT888/n/LycpYsWdLm187NzWXPnj2NHjev/zY3AcvR9OnTCQgIYM2aNRQXF9c7tnTpUtLS0pgwYQLz589nzZo1rFmzhrq6OoYMGUJERESbv4+GHIeh9+7dy+HDh0lISLA9PnPmTAB2794NSAALIRqTAO7iLBYLq1evBrBdQ7z11lsBePXVV9v02nV1dZx//vkMHTrUNtxscvX6r6lHjx6cc845VFdXs2LFinrH/vSnP3H8+HH69evHvHnzWLlyJatXr2b16tV88803bfoemuIYwOYbmMmTJ9vuN87IyGDs2LG250sACyEaku0Iu7itW7dSUFBARkYGp5xyCmAM98bHx7N161a2bNnCyJEjW/XaK1asYOfOnQD8+c9/rldRu1sBm+1at24dH374IVdffTUAmZmZ7Nmzh+joaHbt2kVQkG9+pB0DuKKiAoALLrig3nNmzZrFunXrAM+vgiWE6PhcqoBVVf2rqqrrVFVdrKpqiMPjU1VV3aiq6npVVV/2XjOFt5jV25QpU2yLYYSFhXHDDTcAsHDhwla/9ksvvWT7eNmyZezatcv2uau3IDm67LLLACPYq6qqAFizZg0A5513ns/CF4x2BwcHs3fvXlsbGu7m9Jvf/MY28UoqYCFEQy0GsKqqI4AUTdPGAjuAmQ6HtwPjNE07F4hXVXWUd5opvMUxgB3dcsstgLFdYGlpqduvu3PnTlavXk14eDhXXnkluq7b7outq6uzVcbuBHCfPn0YPnw4paWlttAz/54wYYLbbWyLkJAQW9vLy8sZMmQIaWlp9Z4THR3NnDlziImJqbdghhBCgGsV8FnAKuvHKwHb2neaph3RNK3W+mkNUIvoMEpLS9mwYQMBAQGNAmzw4MGcc845lJaW8u6777r92i+/bAyIXH/99TzzzDMEBQWxZMkS9uzZw7p166isrCQ9PZ2YmBi3XtdxNrSu67YAnjhxotttbCvHdZebugf3mWeeobCwUCpgIUQjrozZxQLHrB8XAfENn6Cq6kggUdO0Rmvzqap6K3ArwJw5cxoN03UGjostdCRr1qyhpqaGESNGUFFR0eh7uOSSS9iwYQPLly9vdH2zKTU1NezcuZM33ngDMIZhg4OD+c1vfsPSpUtRVZWSkhLAWOfZ3X4z1z7+8MMP+e1vf8uxY8dITEwkLi7O5/8Gjrc3nXHGGfXO31F/JrxB+sJO+sKuK/VFw9ExkysBXAiYd/jHAiccD6qqmg78A5jh7Is1TVsImBcSXdu/rYPJyspqsoP9qaysjPDw8CZ3AtqyZQsA06ZNc9r+mTNnMnfuXDZt2kRycjKBgYEtnjMrK4ulS5dSXl7O+eefz6RJkwBjEtby5cspKSkhPj6emTNn8tBDD7ndb6mpqfTt25cDBw7Yrk9PmjTJK6tMtWT8+PGAMRx9+eWX17vdqb3+TPiD9IWd9IWd9IVrQ9CbAHN87QJgg3lAVdVIYAlwu6ZpeZ5vnmitX3/9lbi4OO6//36nx7Oysmx7yDY1fNq3b18yMjIoLCx0eTvAAwcO8MQTTwDwxz/+sd5rrV+/ns8//5zs7GwWLFjQqhWqFEWxDUMvW7YM8P31X9Po0aO57LLLmDdvnlfuNRZCdG4tBrB1WDlbVdV1wGDgfVVVF1gP3w30A15WVfVrVVXP815ThTtWrlxJTU0Nr7zyCoWFhfWOHTt2jPHjx3Ps2DFGjRrV5C46iqLYrq2a11qbU1dXx/33309lZSWzZs1qFOyjR4+2revcFmYAm/xx/ReMyvfDDz/kscce88v5hRAdnK7rvvzTKWVmZvq7CY1ce+21OsaQv/7iiy/aHj927Jg+YMAAHdBPP/10vaCgoNnXWbx4sQ7oF1xwQYvn/Pvf/64DenJycouv2xa1tbV6UlKSDui9e/f22nnaoj3+TPiL9IWd9IVdF+sLp5koK2H5WXV1NZ999hnvvfce7733HmvWrEHX236p3HGnnoULF6Lrum2zgN27dzNs2DC+/PJL4uMbzamrxxzeXbduHdXV1U0+78cff2TevHkAvPLKKy2+blsEBgYyffp0wH/VrxBCtJWshOVnf//733nooYfqPbZ48WJ++9vftvo1y8vL2bVrF4GBgcTFxbF9+3Y2b97Mli1b2LhxIykpKXz55ZckJCS0+FrJyckMGTKEX3/9lU2bNjFu3LhGz/noo4+49tprKS8vZ8aMGVx66aWtbrurHnnkEWpra/nf//1fr59LCCG8QSpgPzO31xs/fryt2nziiSeoq6tr9Wv+8ssvWCwWBg4cyI033ggYGy2Yk6Jefvll2yburmjuOvBzzz3H5ZdfTnl5ObNmzeK5555rdbvd0bNnT15//XWP73QkhBC+IgHsR0VFRWzatImgoCA++ugjVq5cSd++fdm9ezfvvPNOq1/XHH4eMWIEN998M2As31hSUsKMGTO4/PLL3Xo9843B2rVr6z3+008/8eCDD6LrOk8//TT/+c9/CA0NbXW7hRCiK5EA9qOvvvqKuro6zjrrLKKjowkODrZdR33yySdbXQU7BvCpp57KeecZk9Ojo6NtK1S547zzziMgIIBNmzbVW5byk08+AeB3v/sdDz30kG0taSGEEC2TAPajL774Aqh/H+4NN9xARkYGu3btst2n6y7HAAZ4+OGHiY+PZ/78+aSmprr9erGxsYwaNYra2lpbmwE+//xzwFgxSwghhHskgP3I2UYIjlXwE088gcVices1a2tr+eWXXwB7AE+aNImCgoI2Tey68sorAVi0aBEABQUFbNq0ieDgYL8thCGEEB2ZBLCf7N+/nwMHDhAXF9dov93Zs2eTlpbGjh070DTNrdfdtWsXlZWV9OnTh9jYWI+199prryUwMJAVK1aQl5fHqlWr0HWdcePGERUV5bHzCCFEVyEB7Cdm9Ttp0qRGayyHhITYNj/YsGFDo69tTsPhZ09JSkriwgsvpLa2liVLlrBixQoALrroIo+eRwghugoJYD9pah9ek7l/bHsJYIBZs2YB8J///Md2LXjq1KkeP48QQnQFshCHH9TU1Nhu6Wlqe0bHANZ13aUZxnV1dbbA9kYAX3LJJcTGxto2ZsjIyGDgwIEeP48QQnQFXboC/stf/sLgwYNtf+68806PLAPZkh9++IHi4mJOPfVUMjIynD7n1FNPJTExkZycHA4ePNjiaxYXFzN9+nS+//57IiIiOPPMMz3dbMLCwrj66qttn0+dOlVuPRJCiFbqsgGclZXFo48+ys6dO21/5s+f79KuP2319ddfA82vY6woim3z+Y0bNzb7eocOHeKcc85hxYoVJCQksHLlShITEz3WXkfmMDTI9V8hhGiLLhvA//rXv6itrWX69On8+uuvzJ07F4DHH3/c61WwGcDmAhlNceU68MaNGxk9ejTbt29n0KBBbN68mbFjx3qsrQ2deeaZjB07lt69e8vtR0II0QZdMoArKipYsMDY0vgPf/gDgwcP5o9//CNxcXGsX7/eFpCtdeTIERYsWEBFRUWjYzU1NbaKtqUANivgpgJ48eLFjB8/nry8PCZPnszGjRvp169fm9reEkVRWLt2LXv37qVbt25ePZcQQnRmXTKAly5dSkFBASNHjrSFXHR0NP/zP/8DGFVwW9xxxx3cfvvtXHrppY1CeMuWLZSVlTFgwACSk5ObfR1VVQkJCWH79u2cPHmy3rH333+f6667jurqau68805WrFjh0ft+mxMUFERQkMzfE0KItuhyAazrOv/4xz8AuPfee+tNIrr77ruJjY3lm2++4ZtvvmnV6+fm5tpu0Vm9ejUzZsygsrLSdtzV4WcwJj2NHDkSXdfZtGlTvWPLli0DYN68efzzn/+UQBRCiA6mywXwN998w7Zt20hKSrItr2iKiYnhvvvuA4wZ0q3xzjvvUFdXh6qqdO/enS+++IIrrriC2tpa2/kBzj//fJdez7wO3HAilhnI1157bavaKYQQwr86XQAXFRU1Gq51ZFa/t99+u9Ot8+6++25CQkJYvXo1x48fd/v8b7/9NgAPPPAAa9asITExkRUrVrBw4UJqa2tZv3494FoFDM4nYmVmZpKZmUlMTIzchyuEEB1UpwrgqqoqRowYQVJSEnfffTfHjh2rd/zgwYN8/PHHBAcHc/vttzt9jbi4OC644AIsFgvvv/++W+ffu3cv33//PZGRkVxyySUMHTrUNtnr0UcfZc2aNZSWlnLKKae4vCuRYwVcVlYG2KvfM888k4CATvVPKIQQXUan+u393//+l4MHD1JdXc3LL79M3759ef75523H//nPf2KxWLj66qubnQBlDk2/8847bp1/8eLFAFxxxRVEREQAMGPGDM4//3wKCgps99C6Wv0CdO/enTFjxlBZWWlbvvK7774D4KyzznKrfUIIIdqPThXAr7/+OgD33XcfV1xxBVVVVTzwwAOsWrWK0tJS/v3vfwPG5KvmTJ8+ndDQUNatW9eoim6Kruu2AL7uuutsjyuKwt///ncCAgJsQ9ruBDDAZZddBsBHH30E2CtgCWAhhOi4Ok0AHz16lFWrVhESEsLDDz/M8uXL+fOf/wwYqze98MILFBUVcc455zTa/q+h6OhoLrroInRdZ/ny5S6df82aNezbt4+UlBTGjx9f79jw4cO5+eabbZ+3NoA/+eQTysrK2LJlCwCjR49263WEEEK0H50mgN988010XWfGjBnEx8cDMHfuXMaNG0dOTg6PPfYYAPfcc49Lr3fVVVcB8O6777b4XIvFYltJa86cOY22FwT485//TFpaGmeffTY9e/Z0qQ2mAQMGMHDgQAoLC3nppZeoqqpi0KBBxMXFufU6Qggh2o9OEcAWi4X//Oc/ANx00022xwMDA3n77bdtC1Skp6czY8YMl15z2rRphIeHs2HDBo4ePdrscz/99FO2bNlCSkpKk8Pb3bt3Z+/evXz77bcunb8hswp++umnARgzZkyrXkcIIUT70CkC+JtvvuHAgQP07Nmz0QYHPXv25I033iAyMpLHHnuM4OBgl14zMjKSiy++GGhcBW/evJnXXnuNsrIyqqureeaZZwBjBa3mlmcMDw93Wh27wgzgoqIiQK7/CiFER9fhA/jw4cO2qnf27NlOA+7SSy+luLi43nVYV1xzzTWAMbnL3KChvLyciy66iJtvvpk+ffpwzTXXcPjwYQYMGMCNN97Yxu+maaNGjSIlJcX2uQSwEEJ0bB06gI8ePcr48eM5dOgQo0eP5oEHHmjyua3Zt/aSSy4hKSmJHTt22G79eeeddygsLCQ4OJi8vDw++OADwFg5y5vLQQYEBHDppZcCEBUVxaBBg7x2LiGEEN7XYQM4MzOT8ePHc/DgQVRV5YsvviA6Otqj5wgODmb27NkAvPrqqwDMnz8fgAULFrBixQrGjx/PlVdeaRsi9iZzYtiECRNaPZQthBCifVC8vfdtAx472bx58/jLX/7CyJEjWb16tddmBO/bt49TTjmF8PBwPv30UyZOnEhsbCxZWVm2xTaysrJIS0vzyvkbWr9+PQMGDKB79+4+OZ+7fNkX7Zn0g530hZ30hV0X6wunQ7AujZmqqvpXYAxwBLhR07Rq6+NBwKtAf+BHTdOaX+HCg5588knCw8O56667vHo7Tv/+/ZkwYQJr1661XROePXu2LXx97dxzz/XLeYUQQnhWi0PQqqqOAFI0TRsL7ABmOhy+BMiyHotQVfVs7zSzscDAQB555BHbPb/edMsttwDGVoNAk+tICyGEEK5y5RrwWcAq68crgbNdPNZpzJgxg4SEBAAmTpzIgAED/NwiIYQQHZ0rQ9CxgLkgchEQ3+BYcRPHAFBV9VbgVjBWiZo8eXIrm+pfN910E88//zw33XQTWVlZ9Y7V1NQ0eqyrkr4wSD/YSV/YSV/YdaW+aOpatysBXAiY04tjgRMuHgNA07SFwELrpz6d8eVJzzzzDPPmzbOtquWoi00maJb0hUH6wU76wk76wk76wrUh6E3AFOvHFwAbXDzWqSiK4jR8hRBCiNZoMYA1TdsKZKuqug4YDLyvquoC6+FPgHTrsQpN077zXlOFEEKIzsOl25A0TXuwwUO3WR+vBWZ7uE1CCCFEp9dhV8ISQgghOjIJYCGEEMIPJICFEEIIP5AAFkIIIfxAAlgIIYTwAwlgIYQQwg8kgIUQQgg/kAAWQggh/EDR9Q67PLMQQgjRYUkFLIQQQviBBLAQQgjhBxLAQgghhB9IAAshhBB+IAEshBBC+IEEsBBCCOEHEsBCCCGEH0gAu0FV1W7WvxV/t8WfpB/sVFXNsP4tfaGqo/3dhvZCVdV0f7ehPZDfFc2ThThcoKrqFOBWIBt4WtO0LD83yS9UVb0UuAHIwuiHY35ukt9Yf7E8C6QDMzVNq/Fzk/xGVdXhwIvAJuARTdOq/dwkv1FV9SLgLqAS+A+wVtO0Cv+2yvfkd6ZrpAJ2zbXAq8A24DZVVcf6uT0+p6rqhcAs4GkgH/iD9fEu+c5W07QyoAqIAm6ErtsXwFjgKU3THgL6+bsx/qKqaiBwG7AQ483ZuUBwF/25uI4u/jvTFVIBO6GqagRwFbAeyAUeBJ4HyqyPJwDvdvYK0NoP1wArAQtQp2larqqqMcAS4EZN03L92UZfcfiZ+FbTtP3WX6p3Aj8D9wAPaJp2xJ9t9BXH/x+apu1VVfUm4DTgdIyK5wfgE03T9vuvlb5h7YurgW+AUmAO8BGwD1gG3A7kaJpW5a82+oKqquHAo8DnwI/AXLrg70x3SQXcgKqq1wBfAxHAAU3TioAk4Czr0NpWIByI8VsjfcChH8KA45qmZVvDNxDoBhzsQuFr9kU4cARA0zQdGITxc/ABxrv8Tn/dr0FfHLI+HIHxf+R+jDclVcBUPzTPpxr2haZpx4E1wPXATxhv3m8G7vBTE31CVdWewFLgOPCdpmmlQHe62O/M1pAAdqCqajRwJfAkxn+kSaqqJgDzgZtVVY3QNG07kGH90yk56YfzVVUdCKBpWh1GAOvW5/bqzENsDfpiLXCeqqpDrIe/wXi3X4bxS/du69d0yv9XTvpivKqqqcD7QAiQan3DehiotX5Np/zZcPJ/ZKKqqqdomvY1Rt+8qGnaLIyKMExV1YDO2hdAEPBf4CvgXutkvCXALFVVu3WF35mt1eWHoK2zWB8APgM2YFzPug/jF8onGL9Yz8eYUBACrANmA8s1TfvU9y32jhb64WOMfrhM07RDqqreiHF9qwhjaOku67veTsHFvrgA43rf+RiVTjZQqmnaI35oste4+P9jIjABGAzsBC4G9mqa9qQ/2uwtLv5cTAWmAcnAFxhvygo1TbvbH232Bod++AT4BUizfp6F8YZ0NvBn4DIgB9hMJ/yd6Qmd8p26q6xDhs9hDKUlAYs0TVsB/BUYr2nac8BbwLOapj2DEb63ANs60w+SC/3wPPAGxsQSMGb+no3xS3ZWJwtfV/riLeBP1ue9rmna1Zqm3dcJw9eV/x+LgL9qmvYu8C5wFsYwZGcLX3d+Ll4D9mBcE93UycLXsR9SgH9pmqYBiUClpmmLgReAKRjXgDfTCX9nekqXDGBVVcc5DAfFapr2vKZpbwKRqqrO1TRtFWAe/zsQoapqlHV4aZamaS/4vtWe52Y/vIx1WBFjqOksTdPm+7jJXuNmX/wD6/UsTdPetn59p/m/1Iq+CFFVNVrTtB3A/V385yISCNM0bRkwQ9O0l/3QbI9rph+iVVX9HUbFeyaApmmfA0OA4M72O9PTOs0vDVeoqtpNVdVVGNdspmLcTrNeVdXbrE/5Fpiuqmqspml1qqqOwzqjUdO0EgBN02qdvHSH0oZ+2A+gadp6TdNO+r7lnteGvtiraVq5+Tqapll83HSPa+P/j2KwzRHo8Nr4c1EG0Bnuh3axH35n/XujqqqPqKq6GjgGnIDO8TvTW7rcNWBVVc8AegGjgX8CscC/gINACcaEmtUY17JexRhifN8vjfUi6Qc76Qs76Qs76QuDC/1QBXyIMdycBJymadoXfmlsB9PlAtikquqLwPeapr1tnckZjXHv3r3A29ZbCjo96Qc76Qs76Qs76QtDC/2wSNO0PL82sAPqUkPQUO+2iMUYt1H0sN4cHg28g/FOr7QzXdNzRvrBTvrCTvrCTvrC4GI/lHXi26y8pstWwACqqt6NsXTeCYx3cvs0Tfvev63yPekHO+kLO+kLO+kLg/SDZ3Xqd25NcXjHOgy4CGNVpyVd7QdJ+sFO+sJO+sJO+sIg/eAdXb0CvgL4VOvk67S2RPrBTvrCTvrCTvrCIP3gWV06gIUQQgh/6ZJD0EIIIYS/SQALIYQQfiABLIQQQviBBLAQQgjhB0H+boAQwn2qqkYAf8DYCP4NVVVnA/8BHrTuUiSEaOekAhaiY4oAHsPYZxXgG+AajD1ahRAdgFTAQnRMmvXv81RV1YHDQAbwILBbVdVDGHu0LgBuxtjN5hWMvWqDgBs1TVupqmoI8H8Y4d0NY3OBO2VdXyG8TypgITqmeda/d2KEp7Nh525AGPAdMANYiLGBfA/gaetz/gjcj1E5/x1jlaNOs5+vEO2ZBLAQHdMq69+51s3fS508xwLcB5hb5L2ladqLGHu19rE+Ns36920YQ9rdgCleabEQoh4ZghaiY3JlCbsKTdOqVVWtsX5eZP27Dgi0fqwAtRhBXGd9TN6YC+ED8h9NiI6pGKPC7a+q6rUY139b4xOMN+KzMLaVuxCjGhZCeJkEsBAdkKZpNRjXc2OBt7FXr+76i/V1xgIvY1wD/sYDTRRCtEA2YxBCCCH8QCpgIYQQwg8kgIUQQgg/kAAWQggh/EACWAghhPADCWAhhBDCDySAhRBCCD+QABZCCCH8QAJYCCGE8IP/B3LYQVzUz9lqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = RNNModel.load_from_checkpoint(model_name=\"Air_RNN\", best=True)\n",
    "eval_model(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backtesting\n",
    "Let's backtest our `RNN` model, to see how it performs at a forecast horizon of 6 months:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1da9ae4ed7244f24bfbca41d85e51dbc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "backtest_series = my_model.historical_forecasts(\n",
    "    series_transformed,\n",
    "    future_covariates=covariates,\n",
    "    start=pd.Timestamp(\"19590101\"),\n",
    "    forecast_horizon=6,\n",
    "    retrain=False,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 2.43%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "series_transformed.plot(label=\"actual\")\n",
    "backtest_series.plot(label=\"backtest\")\n",
    "plt.legend()\n",
    "plt.title(\"Backtest, starting Jan 1959, 6-months horizon\")\n",
    "print(\n",
    "    \"MAPE: {:.2f}%\".format(\n",
    "        mape(\n",
    "            transformer.inverse_transform(series_transformed),\n",
    "            transformer.inverse_transform(backtest_series),\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monthly sunspots\n",
    "Let's now try a more challenging time series; that of the monthly number of sunspots since 1749. First, we build the time series from the data, and check its periodicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 125)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series_sunspot = SunspotsDataset().load()\n",
    "\n",
    "series_sunspot.plot()\n",
    "check_seasonality(series_sunspot, max_lag=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(series_sunspot, 125, max_lag=240)  # ~11 years seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sp, val_sp = series_sunspot.split_after(pd.Timestamp(\"19401001\"))\n",
    "\n",
    "transformer_sunspot = Scaler()\n",
    "train_sp_transformed = transformer_sunspot.fit_transform(train_sp)\n",
    "val_sp_transformed = transformer_sunspot.transform(val_sp)\n",
    "series_sp_transformed = transformer_sunspot.transform(series_sunspot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f159c4b53ca4bf0a2687c68292b9fc3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0087, validation loss: 0.0208, best val loss: 0.0208\r"
     ]
    }
   ],
   "source": [
    "my_model_sun = BlockRNNModel(\n",
    "    model=\"GRU\",\n",
    "    input_chunk_length=125,\n",
    "    output_chunk_length=36,\n",
    "    hidden_dim=10,\n",
    "    n_rnn_layers=1,\n",
    "    batch_size=32,\n",
    "    n_epochs=100,\n",
    "    dropout=0.1,\n",
    "    model_name=\"sun_GRU\",\n",
    "    nr_epochs_val_period=1,\n",
    "    optimizer_kwargs={\"lr\": 1e-3},\n",
    "    log_tensorboard=True,\n",
    "    random_state=42,\n",
    "    force_reset=True,\n",
    ")\n",
    "\n",
    "my_model_sun.fit(train_sp_transformed, val_series=val_sp_transformed, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate our model, we will simulate historic forecasting with a forecasting horizon of 3 years across the validation set. To speed things up, we will only look at every 10th forecast. For the sake of comparison, let's also fit an exponential smoothing model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "538b4ddfae114d4eb971440bfc42be63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/49 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e86ab53af1247b98b4d4bb77db2cad8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/49 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the backtest predictions with the two models\n",
    "pred_series = my_model_sun.historical_forecasts(\n",
    "    series_sp_transformed,\n",
    "    start=pd.Timestamp(\"19401001\"),\n",
    "    forecast_horizon=36,\n",
    "    stride=10,\n",
    "    retrain=False,\n",
    "    last_points_only=True,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "pred_series_ets = ExponentialSmoothing(seasonal_periods=120).historical_forecasts(\n",
    "    series_sp_transformed,\n",
    "    start=pd.Timestamp(\"19401001\"),\n",
    "    forecast_horizon=36,\n",
    "    stride=10,\n",
    "    retrain=True,\n",
    "    last_points_only=True,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNN MAPE: 71.86777453068686\n",
      "ETS MAPE: 116.63591326597222\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_sp_transformed.plot(label=\"actual\")\n",
    "pred_series.plot(label=\"our RNN\")\n",
    "pred_series_ets.plot(label=\"ETS\")\n",
    "plt.legend()\n",
    "print(\"RNN MAPE:\", mape(pred_series, val_sp_transformed))\n",
    "print(\"ETS MAPE:\", mape(pred_series_ets, val_sp_transformed))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
